Is Single Value#

This notebook provides an overview for using and understanding the Is Single Value check.

Structure:

What is the Is Single Value check#

The IsSingleValue check checks if there are columns which have only a single unique value in all rows.

Imports#

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris

from deepchecks.tabular.checks import IsSingleValue

Load Data#

iris = load_iris()
X = iris.data

Run the check#

IsSingleValue().run(pd.DataFrame(X))
Single Value in Column


If None is given as a value, it will be ignored (this can be changed with ignore_nan set to False):

df = pd.DataFrame({'a': [3, 4, 1], 'b': [2, 2, 2], 'c': [None, None, None], 'd': ['a', 4, 6]})
sv = IsSingleValue()
sv.run(df)
Single Value in Column


# Ignoring NaN values:
IsSingleValue(ignore_nan=True).run(pd.DataFrame({
    'a': [3, np.nan],
    'b': [2, 2],
    'c': [None, np.nan],
    'd': ['a', 4]
}))
Single Value in Column


Ignoring specific columns by name is also possible:

sv_ignore = IsSingleValue(ignore_columns=['b', 'c'])
sv_ignore.run(df)
Single Value in Column


Total running time of the script: ( 0 minutes 0.184 seconds)

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